PIMNet for Lithium-ion Battery Health Modeling Dataset

The four battery degradation datasets\u2014XJTU, TJU, MIT, and HUST\u2014are publicly available lithium-ion battery lifecycle monitoring datasets, widely used for health state estimation, remaining useful life (RUL) prediction, and degradation mechanism modeling. These datasets encompass a variety of battery chemistries, charge\/discharge rates, ambient temperatures, and experimental setups. They provide multi-dimensional sensor data including voltage, current, capacity, and temperature, which collectively offer a comprehensive view of battery performance evolution and degradation behavior under realistic operating conditions. As such, they serve as standardized benchmarks for validating data-driven models and physics-informed hybrid approaches.